Abstract
This study aims to reduce the statistical uncertainty of the correlation coefficient matrix in the mean-variance model of Markowitz. A filtering algorithm based on minimum spanning tree (MST) is proposed. Daily data of the 30 stocks of the Hang Seng Index (HSI) and Dow Jones Index (DJI) from 2004 to 2009 are selected as the base dataset. The proposed algorithm is compared with the Markowitz method in terms of risk, reliability, and effective size of the portfolio. Results show that (1) although the predicted risk of portfolio built with the MST is slightly higher than that of Markowitz, the realized risk of MST filtering algorithm is much smaller; and (2) the reliability and the effective size of filtering algorithm based on MST is apparently better than that of the Markowitz portfolio. Therefore, conclusion is that filtering algorithm based on MST improves the mean-variance model of Markowitz.
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This work is supported by the funds project under the Ministry of Education of the PRC for young people who are devoted to the researches of humanities and social sciences under Grant No. 09YJC790025.
Feixue Huang graduated from Dalian University of Technology of China and is an Associate Professor in the School of Economics at Dalian University of Technology of China. His research interests include system complexity and financial engineering.
Lei Sun got Master degree in 2010 from Economics School, Faculty of Management and Economics, Dalian University of Technology, Dalian China. Her main research interests are portfolio optimization of markowitz and statistical method.
Yun Wang is a master student in Economics School, Faculty of Management and Economics, Dalian University of Technology, China. She will graduate in 2012. Her major is international economics. Her research interests are primary commodities and exchange rates.
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Huang, F., Sun, L. & Wang, Y. Mean-variance model based on filters of minimum spanning tree. J. Syst. Sci. Syst. Eng. 20, 495–506 (2011). https://doi.org/10.1007/s11518-011-5178-6
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DOI: https://doi.org/10.1007/s11518-011-5178-6